Boosting Discriminant Learners for Gait Recognition Using MPCA Features

• Main Authors: Plataniotis KN, Venetsanopoulos AN, Lu Haiping
• Format: Article
• Language: English
• Published: SpringerOpen 2009-01-01
• Series: EURASIP Journal on Image and Video Processing
• Online Access: http://jivp.eurasipjournals.com/content/2009/713183

<p/> <p>This paper proposes a boosted linear discriminant analysis (LDA) solution on features extracted by the multilinear principal component analysis (MPCA) to enhance gait recognition performance. Three-dimensional gait objects are projected in the MPCA space first to obtain low-dimensional tensorial features. Then, lower-dimensional vectorial features are obtained through discriminative feature selection. These feature vectors are then fed into an LDA-style booster, where several regularized and weakened LDA learners work together to produce a strong learner through a novel feature weighting and sampling process. The LDA learner employs a simple nearest-neighbor classifier with a weighted angle distance measure for classification. The experimental results on the NIST/USF "Gait Challenge" data-sets show that the proposed solution has successfully improved the gait recognition performance and outperformed several state-of-the-art gait recognition algorithms.</p>